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Results for “Gene Expression Profiling”

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At least 235 records · Page 13Linked to original sources
PubMed2001

[Gene expression profiling of human ovarian epithelial tumors by digo nucleotide microarray].

Gene expression of human ovarian carcinoma cell lines and epithelial ovarian tumors was examined by oligonucleotide microarray for about 6000 human cDNAs. (1) Comparison of gene expression between CDDP-sensitive human ovarian serous adenocarcinoma cell lines and CDDP-resistant cell lines revealed that gamma-glutamylcysteine synthetase, glutathione peroxidase-like protein, dehydrogenase (UGDH), NAD(P)H: quinoneoxireductase, glucose-6-phosphatase, ornithine decarboxylase and dihydrodiol dehydrogenase were associated with a mechanism of CDDP-resistance. Comparison of gene expression between taxol-sensitive human ovarian cell lines and taxol-resistant cell lines showed that up-regulation of 30 kinds of gene expression including MDR and semaphorin E in taxol-resistant cell lines. (2) Comparison of gene expression among serous adenocarcinomas, clear cell adenocarcinomas and non-cancerous ovarian tissues by hierarchical clustering demonstrated that clear difference between carcinomas and non-cancerous ovarian tissues but not obvious difference between serous and clear adenocarcinomas. Genes that were up- and down-regulated specifically in these two types of ovarian carcinomas were further selected by the criteria that difference in the mRNA level by more than 4-fold between tumors and non-cancerous tissues. Tissue type specific alterations of gene expression are likely to play important roles in the carcinogenesis of epithelial ovarian tumors. cDNA microarray is a powerful and high-throughput tool to analyze gene expression of cancer development.

Adenocarcinoma, Clear Cell↗
PubMed2001

Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles.

Many biological signaling pathways involve autocrine ligand-receptor loops; misregulation of these signaling loops can contribute to cancer phenotypes. Here we present an algorithm for detecting such loops from gene expression profiles. Our method is based on the hypothesis that for some autocrine pathways, the ligand and receptor are regulated by coupled mechanisms at the level of transcription, and thus ligand-receptor pairs comprising such a loop should have correlated mRNA expression. Using our database of experimentally known ligand-receptor signaling partners, we found examples of ligand-receptor pairs with significantly correlated expression in five cancer-based gene expression datasets. The correlated ligand-receptor pairs we identified are consistent with known autocrine signaling events in cancer cells. In addition, our algorithm predicts new autocrine signaling loops that can be verified experimentally. Chemokines were commonly members of these potential autocrine pathways. Our analysis also revealed ligand-receptor pairs with expression patterns that may indicate cellular mechanisms for preventing autocrine signaling.

Algorithms↗
PubMed2002

Gene expression profile from the striatum of amphetamine-treated rats: a cDNA array and in situ hybridization histochemical study.

Changes in gene/protein expression markedly outlast the transient changes in behavior evoked by a single dose of a psychostimulant. These changes in gene expression are thought to underlie and/or trigger enduring changes in neuroplasticity that lead to drug addiction. We used cDNA arrays to gain a more complete picture of changes in striatal gene expression 1 and 3 h after an acute injection of amphetamine. Consistent, reliable gene expression changes were detected when criteria of at least a 1.5-fold difference and three replicate hybridizations using independent samples were performed. Using these criteria, the mRNA for three immediate early genes (IEGs), coding for activity-regulated cytoskeletal-associated protein (Arc), nerve growth factor-induced protein A (NGFI-A; early growth response protein 1) and nerve growth factor-induced protein B (NGFI-B), were upregulated 1 and 3 h after amphetamine as previously described. Novel genes, RL/IF-1 (coding for I kappa B alpha chain) and serum/glucocorticoid-regulated serine/threonine protein kinase (SGK) also were increased throughout the striatum, at 1 but not 3 h. Conversely, amphetamine increased the mRNA coding for the secretogranin II precursor (chromogranin C) only at the 3 h time point when a specific decrease in regulator of G-protein signaling 4 (RGS4) mRNA was also observed. Gene changes and unique patterns of expression were verified by in situ hybridization, providing valuable information about changes in gene expression in response to acute amphetamine.

Amphetamine↗
PubMed2003

[Changes in the gene expression profile of the left heart ventricle during growth in the rat].

Wistar rats of 8, 10 and 12-week-old were chosen for study of the relationship between cardiac growth and its gene expression profile changes during maturation. The ultrasonic parameters of rat hearts were recorded before sacrifice, then total RNA of left ventricle were extracted and gene expression profiles were analyzed by cDNA microarray. During growth from 8 weeks to 12 weeks, the body weight increased by 45.5% (287+/-13 g vs 197+/-10 g), and the increment in the first two-week period was equal to that of the second two-week period. The mass of left ventricle and the posterior wall thickness increased by 27.7% (0.60+/-0.03 g vs 0.47+/-0.02 g) and 23.6% (2.04+/-0.04 mm vs 1.65+/-0.13 mm), respectively, and their increment in the first two-week period was much more than that in the second one. Meanwhile, the gene expression profile of the left ventricle changed significantly, which involved cellular structure, metabolism, oxidative stress, signal transduction, etc. Compared with the 8-week-old rats, these genes were mostly up-regulated in 10-week-old rats, while for 12-week-old rats, the gene expression profile of the left ventricle recovered to the pattern of 8-week-old rats again on the whole. These results suggest that the relationship between the changes in cardiac function and gene expression profile can be analyzed comprehensively with the technique of microarray, and that the changes in gene expression profile of the left ventricle during rat maturation adapt to the physiological growth of heart, which is of benefit for keeping the metabolism balance between materials and energy.

Animals↗
PubMed2001

Oral cancer in vivo gene expression profiling assisted by laser capture microdissection and microarray analysis.

Large scale gene expression profiling was carried out on laser capture microdissected (LCM) tumor and normal oral epithelial cells and analysed on high-density oligonucleotide microarrays. About 600 genes were found to be oral cancer associated. These oral cancer associated genes include oncogenes, tumor suppressors, transcription factors, xenobiotic enzymes, metastatic proteins, differentiation markers, and genes that have not been implicated in oral cancer. The database created provides a verifiable global profile of gene expression during oral carcinogenesis, revealing the potential role of known genes as well as genes that have not been previously implicated in oral cancer.

Aged↗
PubMed2003

Gene expression profiles of I3C- and DIM-treated PC3 human prostate cancer cells determined by cDNA microarray analysis.

Studies from our laboratory and others have shown that indole-3-carbinol (I3C) and its in vivo dimeric product, 3,3'-diindolylmethane (DIM), inhibit the growth of PC3 prostate cancer cells and induce apoptosis by inhibiting nuclear factor (NF)-kappaB and Akt pathways. To obtain comprehensive gene expression profiles altered by I3C- and DIM-treated PC3 cells, we utilized cDNA microarray to interrogate the expression of 22,215 known genes using the Affymetrix Human Genome U133A Array. We found a total of 738 genes that showed a greater than twofold change after 24 h of DIM treatment. Among these genes, 677 genes were down-regulated and 61 were up-regulated. Similarly, 727 genes showed a greater than twofold change in expression, with down-regulation of 685 genes and up-regulation of 42 genes in I3C-treated cells. The altered expressions of genes were observed as early as 6 h and were more evident with longer treatment. Upon cluster analysis, we found that both I3C and DIM up-regulated the expression of genes that are related to the Phase I and Phase II enzymes, suggesting their increased capacity for detoxification of carcinogens or chemicals. We also found that I3C and DIM down-regulated the expression of genes that are critically involved in the regulation of cell growth, cell cycle, apoptosis, signal transduction, Pol II transcription factor and oncogenesis. Real-time reverse transcription-polymerase chain reaction analysis was conducted to confirm the cDNA microarray data, and the results were consistent. We conclude that I3C and DIM affected the expression of a large number of genes that are related to the control of carcinogenesis, cell survival and physiologic behaviors. This may help determine the molecular mechanism(s) by which I3C and DIM exert their pleiotropic effects on PC3 prostate cancer cells; in addition, this information could be further exploited for devising chemopreventive and/or therapeutic strategies for prostate cancer.

Cell Division↗
PubMed2002

Distinction in gene expression profiles of oligodendrogliomas with and without allelic loss of 1p.

Oligodendrogliomas frequently, but not always show sensitivity to chemotherapy and recent studies demonstrated that allelic loss of chromosome 1p is highly associated with this chemosensitivity. To gain insight into the molecular mechanism of such difference, we examined comprehensive gene expression profiles of 11 oligodendroglial tumors, six with and five without 1pLOH (loss of heterozygosity), and two normal brain tissues using the oligonucleotide microarray (GeneChip). Statistically significant numbers of genes were expressed differentially between the two genetic subsets. Clustering analysis separated the tumor subsets well. The tumors with 1pLOH had similar expression profiles to the normal brain for those differentially expressed genes. Many genes showing higher expression in tumors with 1pLOH were presumed to have functions in nervous tissues. Notably, the majority of the 123 genes showing significant expression reduction in tumors with 1pLOH were either on chromosome 1 (50%) or on 19 (10%), and the average expression reduction ratio was about 50% (0.54+/-0.13) possibly reflecting the chromosomal deletion. Thus, the biological difference between the genetic subsets of oligodendroglioma was indeed reflected to gene expression profile, which provided baseline information for further studies to elucidate the mechanism of chemosensitivity in gliomas.

Adult↗
PubMed1999

Gene expression profiles in HTLV-I-immortalized T cells: deregulated expression of genes involved in apoptosis regulation.

Human T-cell leukemia virus type I (HTLV-I) is the etiologic agent of adult T-cell leukemia, an acute and often fatal T-cell malignancy. A key step in HTLV-I-induced leukemigenesis is induction of abnormal T-cell growth and survival. Unlike antigen-stimulated T cells, which cease proliferation after a finite number of cell division, HTLV-I-infected T cells proliferate indefinitely (immortalized), thus facilitating occurrence of secondary genetic changes leading to malignant transformation. To explore the molecular basis of HTLV-I-induced abnormal T-cell survival, we compared the gene expression profiles of normal and HTLV-I-immortalized T cells using 'gene array'. These studies revealed a strikingly altered expression pattern of a large number of genes along with HTLV-I-mediated T-cell immortalization. Interestingly, many of these deregulated genes are involved in the control of programmed cell death or apoptosis. These findings indicate that disruption of the cellular apoptosis-regulatory network may play a role in the HTLV-I-mediated oncogenesis.

Apoptosis↗
PubMed2002

Prediction and uncertainty in the analysis of gene expression profiles.

We have developed a complete statistical model for the analysis of tumor specific gene expression profiles. The approach provides investigators with a global overview on large scale gene expression data, indicating aspects of the data that relate to tumor phenotype, but also summarizing the uncertainties inherent in classification of tumor types. We demonstrate the use of this method in the context of a gene expression profiling study of 27 human breast cancers. The study is aimed at defining molecular characteristics of tumors that reflect estrogen receptor tatus. In addition to good predictive performance with respect to pure classification of the expression profiles, the model also uncovers conflicts in the data with respect to the classification of some of the tumors, highlighting them as critical cases for which additional investigations are appropriate.

Bayes Theorem↗
PubMed2003

DNA microarrays for comparison of gene expression profiles between diagnosis and relapse in precursor-B acute lymphoblastic leukemia: choice of technique and purification influence the identification of potential diagnostic markers.

Microarrays for gene expression profiling are rapidly becoming important research tools for the identification of novel markers, for example, for novel classification of leukemias and lymphomas. Here, we review the considerations and infrastructure for microarray experiments. These considerations are illustrated via a microarray-based comparison of gene expression profiles of paired diagnosis-relapse samples from patients with precursor-B acute lymphoblastic leukemia (ALL), who relapsed during therapy or after completion of treatment. Initial experiments showed that several seemingly differentially expressed genes were actually derived from contaminating non-leukemic cells, particularly myeloid cells and T-lymphocytes. Therefore, we purified the ALL cells of the diagnosis and relapse samples if their frequency was lower than 95%. Furthermore, we observed in earlier studies that extra RNA amplification leads to skewing of particular gene transcripts. Sufficient (non-amplified) RNA of purified and paired diagnosis-relapse samples was obtained from only seven cases. The gene expression profiles were evaluated with Affymetrix U95A chips containing 12 600 human genes. These diagnosis-relapse comparisons revealed only a small number of genes (n=6) that differed significantly in expression: mostly signaling molecules and transcription factors involved in cell proliferation and cell survival were highly upregulated at relapse, but we did not observe any increase in drug-resistance markers. This finding fits with the observation that tumors with a high proliferation index have a poor prognosis. The genes that changed between diagnosis and relapse are currently not in use as diagnostic or disease progression markers, but represent potential new markers for such applications. Leukemia (2003) 17, 1324-1332. doi:10.1038/sj.leu.2402974

Biomarkers↗
PubMed2002

Gene expression profiling of HeLa cells in G1 or G2 phases.

The cell division cycle is regulated through both transcriptional and post-transcriptional mechanisms. The altered expression of a number of genes at the mRNA level is known to be essential for progression through the cell cycle, however, a comprehensive gene expression profile of human cells remains to be completed. Here we sought to monitor the differential gene expression of genes after the transition of G2 cells into G1 prior to the restriction point. GeneChip containing microarrays of oligonucleotides corresponding to over 12 000 human genes were employed to profile differential gene expression in G1 and G2. After three independent experiments the resultant data was filtered and a set of genes was compiled based on at least threefold-altered expression, no background noise in determining expression and observation in all experiments. Our analysis identified 154 genes that were elevated in G2 phase of cells as compared to early G1 phase including 15 novel genes. This number included mRNAs whose upregulation is known to occur in G2 phase. Surprisingly only 19 genes were upregulated in G1 phase, among these six genes were novel. Some of these genes are candidates for transition through early G1. This gene inventory for G1 and G2 phases of cell cycle will provide the basis for understanding of cell cycle delay as a result of DNA damage.

Blotting, Northern↗
PubMed2001

Gene expression profiles of proliferating vs. G1/G0 arrested human leukemia cells suggest a mechanism for glucocorticoid-induced apoptosis.

Glucocorticoids (GC) have pronounced effects on metabolism, differentiation, proliferation, and cell survival (1). In certain lymphocytes and lymphocyte-related malignancies, GC inhibit proliferation and induce apoptotic cell death, which has led to their extensive use in the therapy of malignant lymphoproliferative disorders (2). Most of these effects result from regulation of gene expression via the GC receptor (GR), a ligand-activated transcription factor (3). Although hundreds of genes are regulated by GC (1), how certain biological GC effects relate to individual gene regulation remains enigmatic. To address this question with respect to GC-induced cell cycle arrest and apoptosis, we applied DNA chip technology (4, 5) to determine gene expression profiles in proliferating and G1/G0-arrested (by conditional expression of the CDK inhibitor p16/INK4a) acute lymphoblastic T cells undergoing GC-induced apoptosis. Of 7074 genes tested, 163 were found to be regulated by dexamethasone in the first 8 h in proliferating cells and 66 genes in G1/G0-arrested cells. An almost nonoverlapping set of genes (i.e., only eight genes) was coordinately regulated in proliferating and arrested cells. Analysis of the regulated genes supports the concept that GC-induced apoptosis results from positive GR autoregulation entailing persistent down-regulation of metabolic pathways critical for survival

Apoptosis↗
PubMed2002

Comparison of gene expression profiles between hepatitis B virus- and hepatitis C virus-infected hepatocellular carcinoma by oligonucleotide microarray data on the basis of a supervised learning method.

Gene expression profiles of hepatocellular carcinomas (HCCs) associated with hepatitis B virus (HBV) and hepatitis C virus (HCV) were analyzed and compared. Oligonucleotide microarrays containing >6000 genes and subsequent gene selection by a supervised learning method yielded 83 genes for which expression differed between the two types of HCCs. Expression levels of 31 of these 83 genes were increased in HBV-associated HCCs, and expression levels of the remaining 52 genes were increased in HCV-associated HCCs. The 31 genes up-regulated in HBV-associated HCC included imprinted genes (H19 and IGF2) and genes relating to signal transduction, transcription, and metastasis. The 52 genes up-regulated in HCV-associated HCC included a number of genes responsible for detoxification and immune response. These results suggest that HBV and HCV cause hepatocarcinogenesis by different mechanisms and provide novel tools for diagnosis and treatment of HBV- and HCV-associated HCCs.

Aged↗
PubMed2003

Gene expression profiling of preneoplastic liver disease and liver cancer: a new era for improved early detection and treatment of these deadly diseases?

Hepatocellular carcinoma (HCC) is a multi-step process associated with changes in gene expression. Currently, several technologies enable global gene expression profiling. The number of studies to probe global gene expression profiles of HCC or preneoplastic chronic liver diseases has increased exponentially in recent years. These studies have quickly provided rich information and some additional clues to the genesis of liver cancer. The application of gene expression profiling to preneoplastic liver diseases and HCC is growing in importance and practicality. In this commentary, we review the recent advances in the utilization of global gene expression profiling to liver cancer, which have provided new insight into the molecular mechanisms underlying the development of HCC. We have also discussed the problems related to these new technologies, as well as their contributions and implications. By recognizing the shortcomings, we can reassess our current approaches, which allow us to better design and analyze global gene expression-based experiments. These new approaches will undoubtedly contribute to a better understanding of hepatocarcinogenesis.

Gene Expression Profiling↗
PubMed2003

Gene expression profiles in human non-small and small-cell lung cancers.

Suppression subtractive hybridisation (SSH) was performed comparing normal bronchial epithelial cells with a lung squamous cell carcinoma (SCC) and a metastatic small-cell lung carcinoma (SCLC). The sequence analysis of four cDNA libraries revealed 869 individual sequences. Of these, 342 were tested using northern blots of lung cancer cell lines representing the three major subtypes (SCC, adenocarcinoma, SCLC) which confirmed the differential expression of 236 cDNAs. The extended analysis of 31 randomly chosen fragments confirmed the validity of the approach to identify genes associated with lung cancer development. Additionally, five novel full-length cDNA were isolated encoding the microtubule-associated proteins 1A/1B light chain 3, the epithelial V-like antigen 1 (EVA1), the GTP-binding protein SAR1, a new member of the S100-type calcium binding protein family and a new homeobox-containing gene.

Blotting, Northern↗
PubMed2003

Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients.

MOTIVATIONS AND RESULTS: For classifying gene expression profiles or other types of medical data, simple rules are preferable to non-linear distance or kernel functions. This is because rules may help us understand more about the application in addition to performing an accurate classification. In this paper, we discover novel rules that describe the gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. We also introduce a new classifier, named PCL, to make effective use of the rules. PCL is accurate and can handle multiple parallel classifications. We evaluate this method by classifying 327 heterogeneous ALL samples. Our test error rate is competitive to that of support vector machines, and it is 71% better than C4.5, 50% better than Naive Bayes, and 43% better than k-nearest neighbour. Experimental results on another independent data sets are also presented to show the strength of our method. AVAILABILITY: Under http://sdmc.lit.org.sg/GEDatasets/, click on Supplementary Information.

Algorithms↗
PubMed2000

Toward a molecular classification of the gliomas: histopathology, molecular genetics, and gene expression profiling.

As many as 100,000 new cases of brain tumor are diagnosed each year in the United States. About half of these are primary gliomas and the remaining half are metastatic tumors and non-glial primary tumors. Currently, gliomas are classified based on phenotypic characteristics. Recent progress in the elucidation of genetic alterations found in gliomas have raised the exciting possibility of using genetic and molecular analyses to resolve some of the problematic issues currently associated with the histological approach to glioma classification. Recently, immunohistochemical studies using novel proliferation markers have significantly advanced the assessment of tumor growth potential and the grading criteria of some tumor subtypes. Preliminary studies using cDNA array technologies suggest that the profiling of gene expression patterns may provide a novel and meaningful approach to glioma classification and subclassification. Furthermore, cDNA array technologies may also be used to identify candidate genes involved in glioma tumor development, invasion, and progression. This review summarizes current glioma classification schemes that are based on histopathological characteristics and discusses the potential for using cDNA array technology in the molecular classification of gliomas.

Brain Neoplasms↗